April 13, 2024


Super Technology

How Computer Vision Can Create Smart Transportation Systems

How Computer Vision Can Create Smart Transportation Systems

Keyword phrase: smart transportation

Bustling cities need the requisite transportation networks that can keep them running smoothly. AI and computer vision-powered smart transportation enables smart cities to achieve that objective with ease.

There are three attributes that every smart city—or any place of human settlement for that matter—must possess in abundance—livability, workability and sustainability. The frameworks and amenities which allow inhabitants to live comfortable, clean, healthy and safe lives boost the livability quotient of a smart city. Additionally, the communication and mobility networks that make it easier for inhabitants to commute to and from work, expand employment avenues and simplify business creation and growth improve the workability aspect of such cities. And lastly, the sustainability of a smart city is dependent on how well it uses technology to reduce energy consumption, pollution and accidents. As you can see, the role of transportation networks and technologies is massive in deciding how livable, workable and sustainable smart cities can be. Smart transportation holds the key to making vehicular networks in smart cities highly connected and safe for this purpose.

Already, computer vision can be used in countless different applications in smart cities. One of the vital application areas is smart transportation. AI, IoT and computer vision bring their unique capabilities to the table to transform transportation networks and vehicles in smart cities in multiple ways.

Computer Vision for Accident Prevention

According to CDC, approximately 1.35 million people become victims of motor vehicle accidents every year, with about 3,700 daily road accident deaths. A massive chunk of these deaths every year are pedestrians, cyclists and motorcyclists. Such accidents occur due to a number of factors such as poor visibility, driver fatigue, a lack of focus and technical failure, among other reasons. Smart transportation involves an IoT network that includes data receptor sensors installed on highways and busy streets as well as motor vehicles. These sensors, along with computer vision-powered CCTV cameras, can provide timely information to vehicle drivers about how close their vehicles are to pedestrians, static structures or other vehicles at any given instance. In a hyper-connected smart city, this information is first captured autonomously by smart cameras. Then it can be sent to connected vehicles. Once a driver receives the data in their vehicle’s infotainment system, they can slow down or take an alternative route. The algorithms in computer vision systems can predict potential accident systems in advance to reduce avoidable mishaps.

Today, dynamic image capturing and processing are important parts of vehicles. Vehicles in smart cities need computer vision tools for occupant and pedestrian safety. Additionally, the visual data collected and analyzed by computer vision applications can be used by municipalities and other public agencies for creating smart community initiatives.

As with any computer vision or AI-based technology, smart transportation applications keep improving with time. So, the visual data receptors improve in identifying highway symbols and features, lane markings, obstacles and other road-related details. Additionally, such systems become markedly better in detecting objects and potential collisions and relaying the information to vehicle drivers and pedestrians—via smartphone apps or wearable technology.

Safety technologies such as intuitive emergency braking, lane centering, blind-spot safety monitoring, collision alert systems, adaptive cruise control, intelligent speed adaptation systems, night vision systems, pedestrian and road sign recognition systems heavily rely on digital image capturing and cognitive information processing facilitated by computer vision systems. In the current age of big data, computer vision makes it possible to analyze raw information on the fly via algorithms performing several million probability-based calculations and generating valuable insights and forecasts to optimize road safety.

Smart transportation also boosts the sustainability aspect of smart cities. For example, machine learning devices in public transport vehicles allow transport agencies to know the routes with the highest number of travelers. This information allows the agencies to reduce the number of vehicles in areas where the number of travelers is generally low. As a result, fuel and vehicle usage can be controlled. Smart cities such as Amsterdam actively prioritize sustainability and citizen safety while making urban development plans. In addition to safety and sustainability, computer vision can boost mobility in smart cities through intelligent traffic management.

Computer Vision for Traffic Management

As implied earlier, smart transportation is not solely related to vehicle-based tools and applications. The concept also involves the optimization of road networks in a smart city. Smooth mobility is a vital aspect for gauging the livability, workability and sustainability of any smart city. For example, if there is a medical emergency and an individual needs to be rushed to the hospital, having a mobile transportation network can be life-saving in such a situation. Similar examples of mobility being useful for boosting sustainability and workability of smart cities can be witnessed on a daily basis too. So, how exactly does computer vision aid traffic management and, by extension, mobility in smart cities?

Traffic automation mechanisms that cannot be automated generally tend to have several errors in them. So, in the era of rapidly rising daily commuters, shortened or narrow roads and globalization, traffic monitoring becomes an area of concern for local administrators in any region. Smart cities feature some of the busiest highways and an incredibly high number of vehicles, increasing the concern level.

Computer vision-powered smart transportation tools, along with IoT network devices, facilitate autonomous traffic monitoring and communication. Smart traffic lights, smart parking and traffic guidance systems use computer vision and IoT for assisting drivers about available lanes that can lead them to their destination in lesser time. Vehicle-mounted data receptors and connected mobile apps complement computer vision-based smart transportation applications for this purpose.

Smart roads and highways are also a part of a city’s smart transportation network. These concepts feature IoT and AI-based applications in roads and vehicles to control the speed limit of vehicles on all kinds of roads. Such applications continuously and autonomously monitor the flow of traffic and send alerts to drivers and traffic police personnel if vehicles are found crossing the speed limit. This feature of smart transportation is particularly helpful for police authorities as it prevents them from becoming overwhelmed by the high traffic volume and speed in smart cities.

Geospatial traffic guidance systems, also a part of computer vision-powered smart transportation networks, use GPS, GIS and radiofrequency devices for detailed traffic monitoring. Such tools work in unison to provide 3D spatial and geographical information to traffic controllers and drivers regarding vehicle proximity, traffic density, upcoming obstacle-related alerts and traffic inflows on specific routes.

One of the examples of smart city-based traffic management can be found in the city of Darmstadt, Germany.

Computer Vision for Autonomous Driving

Over the last few years, nearly all major carmakers have dipped their feet into the driverless vehicle market. The number of autonomous vehicles is slated to grow over the next few years. Such vehicles will represent the next generation of smart transportation in smart cities. What’s more, such vehicles also feature a plethora of technology, including computer vision applications, that will positively influence their mobility, occupant and pedestrian safety and fuel efficiency-related factors.

Autonomous vehicles rely on several smart cameras for object recognition—for instance, accurately identifying pedestrians, traffic lights, or other vehicles even in moderate to low visibility—in order to regulate safety features such as airbags and automatic brakes. Additionally, such vehicles also rely on computer vision for 3D mapping for better decision-making regarding route selection, driving speed and parking. This results in an even lesser number of accidents in connected smart cities.

In addition to this, computer vision, IoT and AI also make autonomous vehicles more “connected,” meaning they can “communicate” autonomously with other smart vehicles and with smart transportation devices and applications. So, for instance, two autonomous vehicles in a narrow lane can foresee an accident due to a vehicle speeding towards them from the opposite side. Using IoT and computer vision, both vehicles can either pull over to safety or change lanes in perfect sync to avoid a collision and the resultant vehicular pile-up. This is arguably the most basic example of what connected smart vehicles can do.

Smart transportation simply wouldn’t exist without computer vision, AI, IoT, blockchain and a few other smart city technologies. Computer vision, in particular, is instrumental in analyzing dynamically captured data and using it across vehicles and transportation regulation devices. In this way, one can say with certainty that computer vision plays its part in making smart cities more livable, sustainable and workable.